Question

Consider following statements :
A. Regression predicts categorical outcomes.

B. Classification models are used for continuous variable prediction.

C. Clustering is an unsupervised learning task.

D. Reinforcement learning uses rewards and penalties to improve performance.

E. Decision tree can be used for both classification and regression

Choose the correct answer from the options given below :

• A. A, B, E only
• B. A, C, D only
• C. C, D, E only
• D. B, C, E only

Hint:

To remember the difference: Regression = Real numbers (continuous). Classification = Categories (discrete).

Answer 1

The Correct Option is C

We evaluate the fundamental definitions of machine learning paradigms. 1. Evaluating A and B (Supervised Learning):
• Regression is used for predicting continuous numerical values (e.g., house prices). Statement A is incorrect.
• Classification is used for predicting categorical labels or discrete classes (e.g., Spam vs. Not Spam). Statement B is incorrect. 2. Evaluating C, D, and E:
• Clustering (like K-Means) is a core task of unsupervised learning as it finds patterns in unlabeled data. Statement C is correct.
• Reinforcement Learning relies on an agent interacting with an environment to maximize cumulative rewards and minimize penalties. Statement D is correct.
• Decision Trees are versatile algorithms that can be implemented as Decision Tree Classifiers or Decision Tree Regressors. Statement E is correct. Conclusion: Only C, D, and E are correct.